Book Image

Hands-On Machine Learning with ML.NET

By : Jarred Capellman
Book Image

Hands-On Machine Learning with ML.NET

By: Jarred Capellman

Overview of this book

Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET.

Related Content you might be interested in

Current Title:

Small book image
Hands-On Machine Learning with ML.NET
Jarred Capellman
Mar, 2020 | 296 pages
No titles found
Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Fundamentals of Machine Learning and ML.NET
Section 1: Fundamentals of Machine Learning and ML.NET
Free Chapter
2
Getting Started with Machine Learning and ML.NET
Getting Started with Machine Learning and ML.NET
The importance of learning about machine learning today
The model building process
Exploring types of learning
Exploring various machine learning algorithms
What is ML.NET?
Summary
3
Setting Up the ML.NET Environment
Setting Up the ML.NET Environment
Setting up your development environment
Creating your first ML.NET application
Evaluating the model
Summary
4
Section 2: ML.NET Models
Section 2: ML.NET Models
5
Regression Model
Regression Model
Breaking down regression models
Creating the linear regression application
Creating the logistic regression application
Evaluating a regression model
Summary
6
Classification Model
Classification Model
Breaking down classification models
Creating a binary classification application
Creating a multi-class classification application
Evaluating a classification model
Summary
7
Clustering Model
Clustering Model
Breaking down the k-means algorithm
Creating the clustering application
Evaluating a k-means model
Summary
8
Anomaly Detection Model
Anomaly Detection Model
Breaking down anomaly detection
Creating a time series application
Creating an anomaly detection application
Evaluating a randomized PCA model
Summary
9
Matrix Factorization Model
Matrix Factorization Model
Breaking down matrix factorizations
Creating a matrix factorization application
Evaluating a matrix factorization model
Summary
10
Section 3: Real-World Integrations with ML.NET
Section 3: Real-World Integrations with ML.NET
11
Using ML.NET with .NET Core and Forecasting
Using ML.NET with .NET Core and Forecasting
Breaking down the .NET Core application architecture
Creating the stock price estimator application
Exploring additional production application enhancements
Summary
12
Using ML.NET with ASP.NET Core
Using ML.NET with ASP.NET Core
Breaking down ASP.NET Core
Creating the file classification web application
Exploring additional ideas for improvements
Summary
13
Using ML.NET with UWP
Using ML.NET with UWP
Breaking down the UWP architecture
Creating the web browser classification application
Additional ideas for improvements
Summary
14
Section 4: Extending ML.NET
Section 4: Extending ML.NET
15
Training and Building Production Models
Training and Building Production Models
Investigating feature engineering
Obtaining training and testing datasets
Creating your model-building pipeline
Summary
16
Using TensorFlow with ML.NET
Using TensorFlow with ML.NET
Breaking down Google's Inception model
Creating the WPF image classification application
Additional ideas for improvements
Summary
17
Using ONNX with ML.NET
Using ONNX with ML.NET
Breaking down ONNX and YOLO
Creating the ONNX object detection application
Exploring additional production application enhancements
Summary
18
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Breaking down anomaly detection

As mentioned in Chapter 1, Getting Started with Machine Learning and ML.NET, anomaly detection, by definition, is an unsupervised learning algorithm. This means that the algorithm will train on data and look for data that does not fit the normal data. In this section, we will dive into use cases for anomaly detection and into the various trainers available for anomaly detection in ML.NET.

Use cases for anomaly detection

Anomaly detection, as you might have realized already, has numerous applications where data is available but it is unknown whether there is an anomaly in the data. Without needing to do manual spot-checking, anomaly detection algorithms train on this data and determine whether there are any anomalies. ML.NET provides various anomaly detection values to look at programmatically inside of your application. We will review these values later on in this chapter to better ensure that any detection is not a false positive.

Some of the potential...

Previous Section
End of Section 2
Next Section